HP Printer Case

Management in Engineering

November 14, 2012 Dr. Abbott Weiss, Senior Lecturer

 

 

 

 

What do you recommend HP should do?

Universal power supply » Yes ? » No ?

Why?

11/17/12 2

Mfg. Eng.

Mktg.Controller

HP managers around the table

Prod. Design 11/17/12 3

Measurements and Behavior

Five functional managers are discussed in the case:

a.) Marketing b.) Product Design & Development c.) Finance d.) Manufacturing Engineering e.) Distribution

Distribution

How are they measured? How does it influence their views on the universal power supply?

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Universal Power Supply - Costs & Benefits

Costs Benefits

higher cost per unit = $50 lengthen Break-Even Time (BET)? problems allocating supply

increase forecast accuracy fewer stockouts fewer lost sales less safety stock required fewer expedited shipments eliminates re-configuration work

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Key Consequences of this Decision

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Probability of Worldwide Demand

HP Network Printer Demand Worldwide

60%

50%

40%

30%

20%

10%

0%

50%

Average demand = 25,000 per month Average forecast error = 40% at maturity

31% 31%

16% 16%

1% 2%

7% 7%

2% 1%

- 10,000 20,000 30,000 40,000 50,000 60,000

Monthly Demand in Units

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Prob

abili

ty o

f Mon

thly

Dem

and

Cumulative Demand Probability Curve

HP Network Printer Demand - Cumulative Probability

1% 2% 7%

16%

31%

50%

69%

84%

93% 98% 99%

0%

20%

40%

60%

80%

100%

120%

- 10,000 20,000 30,000 40,000 50,000 60,000

at least this Monthly Demand Worldwide

Cum

ulat

ive

Prob

abili

ty o

f Dem

and

Average demand = 25,000 per month Average forecast error = 40% at maturity

2 Standard Deviations gives 98% coverage of possible forecast maxima.

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What happens to forecast error if we have a universal power supply?

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What happens to forecast error if we have a universal power supply?

The variability of the forecast errors would combine as follows:

21 2 2

2 1 2 σρσσσσ ++= new

where: σ

1,2 are the individual product forecast error standard deviations ρ is the correlation coefficient of the two errors

Let’s say your individual forecast value is 1.0 and σ 1,2 both equal 0.4 (40%). If the errors are completely

uncorrelated (ρ =0), then the standard deviation of the forecast error of the combined product stream would be 0.57 or 28% of the combined forecast of 2.0

If people tend to buy one product or the other, and a drop in one always occurs with a rise in the other (perfect negative correlation, ρ = -1), then the new standard deviation would be 0.0

If, say, world events always cause similar errors in both products (perfect positive correlation, ρ = 1) then the new standard deviation would be 0.80 or 40% of the combined forecast.

So you can only get better by combining products.

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What happens to forecast error if we have a universal power supply?

The variability of the forecast errors would combine as follows:

21 2 2

2 1 2 σρσσσσ ++= new

At start-up the individual errors are 80%+. If the errors tend to offset each other, then the combined error will be closer to 40%.

where: σ

1,2 are the individual product forecast error standard deviations ρ is the correlation coefficient of the two errors

Let’s say your individual forecast value is 1.0 and σ 1,2 both equal 0.4 (40%). If the errors are completely

uncorrelated (ρ =0), then the standard deviation of the forecast error of the combined product stream would be 0.57 or 28% of the combined forecast of 2.0

If people tend to buy one product or the other, and a drop in one always occurs with a rise in the other (perfect negative correlation, ρ = -1), then the new standard deviation would be 0.0

If, say, world events always cause similar errors in both products (perfect positive correlation, ρ = 1) then the new standard deviation would be 0.80 or 40% of the combined forecast.

So you can only get better by combining products. At maturity the individual errors are ~40%. If the errors tend to offset each other, then the combined error will be closer to 20%.

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Key Learnings for Management in Engineering

Engineering/design decisions have major impact on operations and customer service

Consider all the costs, especially when things do not go according to plan

Measurements and rewards change behavior, influence how your company operates

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Key Takeaways

1. Forecasts are always wrong. 2. How wrong? (a) a lot, or (b) an awful lot 3. Challenge for international markets: power, localization, etc. 4. Global supply lines mean long lead times, aggravating the problem. 5. Design can have a major impact on supply chain flexibility. 6. �Hard� costs will lead you to specialized products. Inventory

benefits can be very large, but are �Soft� costs. 7. Who is measured on inventory? 8. Hidden costs are often invisible, or occur much later than the key

decisions which can create them. 9. Do the math. Think again about what could change the answer. 10. Remember that this is one of many decisions over time.

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Key Learnings for Supply Chain Management

Value of postponement

Organizational roles and measurements

International dimensions

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Value of Postponement HP Network Printer Demand Worldwide

60%

40%

30%Reduced cycle times 50%

20%

0% - 10,000 20,000 30,000 40,000 50,000 60,000

50%

Average demand = 25,000 per month Average forecast error = 40% at maturity

31% 31%

16% 16%

1% 2%

7% 7%

2% 1%

Monthly Demand in Units Lower forecast errors 10%

Smaller safety stocks/fewer stockouts Lower obsolescence costs Reduced penalty costs/profit drains

Prob

abili

ty o

f Mon

thly

Dem

and

» Reconfiguration and extra handling » Premium transportation » Prevent lost revenue and profit » Prevent loss of market share

Changes during product life cycle

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Organizational roles and Measurements

Marketing Engineering/Design/Product Management Finance Manufacturing Procurement Logistics/Distribution General Managers Supplier/partner

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Organizational roles and Measurements

Accountability BET (Break-Even Time)

How to measure? Who pays?

Effects of regional P&L�s

Costs » Product cost » Transportation » Inventory » Obsolescence » Stockouts

Net profit

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International Aspects

Product variety Distance and time for supply Power and regulatory requirements Labeling, packaging Forecast complexities Supplier inflexibility Accountability and measurement

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